Method and A Device for Denying or Nullifying a Specific Online Transaction Carried Out by an Authorized User who is Coached by a Fraudster

ABSTRACT

A method for denying or nullifying a specific online transaction carried out by a specific user using a computing device associated with at least one input interface, while the specific user was coached by a fraudster. The method includes collecting a specific set of behavioral data relating to the behavior of the specific user during a specific online transaction, and using a multi-dimensional classification module to determine a probability that the specific user was coached during collection of the set of behavioral data. In response to the probability being greater than a predefined threshold, the specific transaction is denied or nullified.

FIELD AND BACKGROUND OF THE DISCLOSED TECHNOLOGY

The disclosed technology relates generally to authentication devices andmethods, and, more specifically, to a device and a method for denying ornullifying a coached fraudulent transaction, which is an onlinetransaction carried out by an authorized user, while the authorized useris coached by a fraudster, for example over the phone. Such coachedfraudulent transactions are also known as vishing attacks.

Electronic devices are used by millions of people to perform many typesof operations, such as communicating with other people (e.g. by email,instant messaging, phone calls, and video chats), capturing memories(e.g. taking pictures, videos, and voice recordings), entertainment(e.g. listening to music, watching videos, playing games), financialtransactions (e.g. access to bank accounts, transferring funds,shopping) and the like.

Some of the more sensitive transactions that may be carried out usingelectronic devices, such as transactions requiring transfer of funds(e.g. shopping, bank account transactions, and the like), requireauthentication of the user in order to ensure that the user carrying outthe transaction is indeed the human authorized to do so.

In an attempt to get around the authentication requirements, criminalsand fraudsters have developed different types of attacks in which theauthorized user is authenticated, but the transaction is a fraudulenttransaction, not the transaction the authorized user thinks he/she isconducting.

One such type of attack, is a “phishing” attack, in which the fraudstercreates a fraudulent log-in interface or sends a fraudulent request,posing as an actual website or an authorized service provider. Theunsuspecting user then provides their authentication information ortheir restricted information (such as bank account or credit cardinformation) enabling the attacker to steal the user credential and usethem freely for purposes of fraud and theft. There are many mechanismknown in the art for detection of such phishing attacks.

Another type of attack is known as a “vishing” attack, in which afraudster poses as an authorized service provider, and guides anunsuspecting authorized user through the various steps of performing anelectronic financial transaction. For example, the fraudster maytelephone the victim and provide oral instructions for performing thetransaction. However, the transaction is a fraudulent transaction. Forexample, the attacker may guide the user to access an authentic website,such as their actual bank account, and to wire money to a specific bankaccount number of the attacker, while pretending that this is requiredin order to move the bank account of the victim to a safer account, orto open a pension fund or an insurance fund for the victim.

Vishing attacks are difficult to detect, because the user conducting theelectronic transaction is the authorized user using his/her standardelectronic device and IP address, and providing his/her actualauthentication credentials of the authorized user. In fact, any securitymeasures aimed to authenticate the identity of the user, such astwo-factor authentication or use of biometric data, would be ineffectivefor identifying a vishing attack, because the authorized user is the onecarrying out the transaction.

It has been discovered that vishing attacks may be detected by detectingbehavioral traits of the user. For example, a user being coached throughan operation, may be waiting to receive the next instruction from thecoaching fraudster, which wait time does not exist when the userperforms the same transaction of their own volition, without beingcoached.

U.S. Patent Application Publication No. 2019/0158535 to Kedem et aldescribes a system for detecting a vishing attack, and relates tovarious detectors for behavior, such as a data entry rhythm detector, aspatial characteristics detector, a doodling detector, and atypographical errors rhythm detector.

There is thus a need in the art for a system and method for denying ornullifying coached fraudulent transactions, which system is a learningsystem automatically learning the thresholds and weights assigned toeach of various input parameters in order to identify coached fraudulenttransactions at a high confidence.

SUMMARY OF THE DISCLOSED TECHNOLOGY

The disclosed technology relates generally to authentication devices andmethods, and, more specifically, to a device and a method for denying ornullifying a coached fraudulent transaction or vishing attack, which isan online transaction carried out by an authorized user, while theauthorized user is coached by a fraudster, for example over the phone.

In the context of the present specification and claims, the term“dataset” or “set of data” is defined as a data sample including all thedata collected during a single recorded user session, or during a singlespecific online transaction.

In the context of the present specification and claims, the term“approximately” is defined as being within 10% of a target number ormeasure.

It should be understood that the use of “and/or” is defined inclusivelysuch that the term “a and/or b” should be read to include the sets: “aand b,” “a or b,” “a,” “b.”

According to an aspect of some embodiments of the teachings herein,there is provided a method for denying or nullifying a specific onlinetransaction carried out by a specific user using a computing deviceassociated with at least one input interface while the specific user wascoached by a fraudster. The method includes collecting, from thecomputing device, a specific set of behavioral data relating to thebehavior of the specific user during a specific online transaction, thespecific user being authorized for carrying out the specific onlinetransaction, and using a multi-dimensional classification model,determining a probability that the specific set of behavioral data wascollected while the specific user was coached by a third party.Subsequently, the probability is compared to a predefined threshold, andin response to the probability being higher than the predefinedthreshold, indicative of the specific user having been coached duringthe specific online transaction, the specific online transaction isdenied or nullified.

The multi-dimensional classification model is trained prior to thedetermining, using a plurality of training sets of behavioral datarelating to the behavior of one or more users during an onlinetransaction, where each specific training set is associated with aclassification indicating whether the specific training set wasgenerated when the user was coached during the online transaction.

Each of the plurality of training sets and the specific set ofbehavioral data includes at least two behavioral parameters selectedfrom the group consisting of:

-   -   a total timespan from selecting a text field for input        thereinto, to leaving the text field, for at least one of a text        field relating to a recipient account identifier, a text field        relating to a recipient name, and a text field relating to an        amount;    -   a number of times during a corresponding online transaction that        a corresponding user stops moving a cursor;    -   a number of times during a corresponding online transaction that        at least one of a plurality of cursor criteria is outside of a        corresponding predetermined range;    -   a timespan between selecting the text field relating to a        recipient name and beginning to enter input into the text field        relating to a recipient name;    -   a total time spent on a monetary transfer page during the        corresponding online transaction;    -   a total time during which a cursor was immobile while        interacting with the monetary transfer page during the        corresponding online transaction;    -   a timespan between selecting the text field relating to a        recipient account identifier and beginning to enter input into        the text field relating to a recipient account identifier; and    -   a number of cursor engagements in the monetary transfer page        during the corresponding online transaction.

In some embodiments, the at least one input interface includes a mouse.In such embodiments, the cursor engagements include mouse clicks, andthe cursor criteria include, for a specific mouse gesture, at least oneof the following criteria:

-   -   a ratio between the shortest distance between two endpoints of        the specific mouse gesture and the length of the specific mouse        gesture;    -   a linearity measure indicating how similar the specific mouse        gesture is to a straight line;    -   a ratio between the length of the specific mouse gesture and the        length of a perimeter of a rectangle enclosing the specific        mouse gesture;    -   a maximal change in the x-direction during the mouse gesture;        and    -   a maximal change in the y-direction during the mouse gesture.

In some embodiments, the specific online transaction is a bankingtransaction.

In some embodiments, the specific set of behavioral data includes datarelating to the entirety of the specific online transaction.

In some embodiments, the collecting and the determining are carried outin real time, during the specific online transaction. In suchembodiments, in response to the probability being higher than thepredefined, the specific online transaction is denied.

In some embodiments, the determining is carried out following completionof the specific online transaction. In such embodiments, in response tothe probability being higher than the predefined, the specific onlinetransaction is nullified.

In some embodiments, the method further includes, following the denyingor the nullifying, notifying at least one of the specific user, a partywith whom the specific transaction was carried out, or an enforcementauthority, of the denying or the nullifying.

In some embodiments, the method further includes following thecollecting and prior to the determining, transmitting at least part ofthe specific set of behavioral data to a server, and wherein thedetermining is carried out at the server.

In some embodiments, the collecting includes collecting at least part ofthe specific data set. In some such embodiments, the collecting, thedetermining, and the comparing are carried out iteratively until theprobability exceeds the predefined threshold or until all data in thedata set is collected.

In some embodiments, the method further includes, in response to theprobability being lower than or equal to than the predefined threshold,indicative of the specific transaction being an authorized transaction,providing a safe transaction notification.

In some embodiments, the providing includes providing the notificationto at least one of the specific user, a party with whom the specifictransaction was carried out, or an enforcement authority that thespecific transaction was a safe transaction.

Any device or step to a method described in this disclosure can compriseor consist of that which it is a part of, or the parts which make up thedevice or step. The term “and/or” is inclusive of the items which itjoins linguistically and each item by itself. “Substantially” is definedas “at least 95% of the term being described” and any device or aspectof a device or method described herein can be read as “comprising” or“consisting” thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is flowchart of a method for denying or nullifying a specificonline transaction carried out while the user was coached by a fraudsteraccording to an embodiment of the teachings herein.

FIG. 1B is a flowchart of a method for creating a multi-dimensionalclassification model suitable for use in the method of FIG. 1A accordingto an embodiment of the disclosed technology.

FIG. 2A is a block diagram of a system for denying or nullifying aspecific online transaction carried out while the user was coached by afraudster according to embodiments of the disclosed technology.

FIG. 2B is a high level block diagram of devices used to carry outembodiments of the disclosed technology.

A better understanding of the disclosed technology will be obtained fromthe following detailed description of the preferred embodiments taken inconjunction with the drawings and the attached claims.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE DISCLOSED TECHNOLOGY

In an embodiment of the disclosed technology, a multi-dimensional,learning classification model is used to identify or classify whether acomputerized or online transaction is a coached fraudulent transaction,based on behavioral information relating to a user carrying out thetransaction. Based on the likelihood of the computerized or onlinetransaction being a coached fraudulent transaction, the system may denyor nullify the transaction. The classification may be carried out inreal-time, or in retrospect after the transaction has been completed.

Embodiments of the disclosed technology will become clearer in view ofthe following description of the drawings.

Reference is now made to FIG. 1A, which is a flow chart of a method fordenying or nullifying a specific online transaction carried out whilethe user was coached by a fraudster according to an embodiment of theteachings herein.

At an initial step S200 of the method, at least part of a specific setof behavioral data relating to the behavior of a specific user during aspecific online transaction is collected. For example, the specifictransaction may be a banking transaction, a shopping transaction, or aninsurance related transaction. The specific user is an authorized user,authorized to carry out the specific transaction, and providing properauthentication information as required for the transaction.

At step S202, a multi-dimensional classification model is used todetermine a probability that the specific set of behavioral data wascollected while the specific, authorized, user was being coached by athird party. Stated differently, at step S202 the likelihood that thespecific transaction was a vishing attack, is determined, using aclassification model. In some embodiments, the classification model is alearning classification model, which was generated in advance of stepS202, for example as described hereinbelow with respect to FIG. 1B.

At step S204, the probability determined by the classification model iscompared to a predefined threshold. The probability obtained from theclassification model exceeding the predefined threshold is indicative ofthe specific user having been coached during the specific onlinetransaction.

In response to the probability being higher than the predeterminedthreshold, if the predefined threshold is exceeded, the specific onlinetransaction is denied (if the determination is made in real-time) or isnullified (if the determination is retroactive) at step S206. In someembodiments, following a determination that the transaction is a coachedfraudulent transaction, a report of the fraudulent transaction may besent to an operator of the system (such as an operator of a server onwhich the transaction was carried out) and/or enforcement authorities,such as a cyber crime department of the police, at step S208. In otherembodiments, before denying the transaction and after determining that aspecific online transaction is being coached, the user is prompted toconfirm that he or she wishes to proceed with the transaction. Thisprompt can include a warning that the person might be currently beingduped into making a dangerous transaction and may ask the person if theyare currently receiving instructions from another party to carry out thetransaction. Based on the answers received to such queries to the user,the transaction may then be declined or voided in such embodiments ofthe disclosed technology.

In some embodiments, the classification model and/or the predefinedthreshold are stored in a central server, and the determination ofprobability at step S202 and the comparison to the predefined thresholdS204 are carried out at the central server, for example as illustratedin FIGS. 2A and 2B. In some such embodiments, the data set collected atstep S200, or a processed or modified version thereof, is provided tothe central server, prior to step S202.

The sets of behavioral data collected at step S200 typically includesmultiple behavioral parameters. The data is typically collected withrespect to completion of an online form, which may include text fields,require cursor movement between fields, and involve operations carriedout by input interface(s) such as a mouse and/or a keyboard.

In some embodiments, the behavioral parameters collected at step S200are processed in real-time, as each behavioral parameter is collected.In some such embodiments, each time one or more behavioral parametersare collected at step S200, or are provided to the central server, theflow moves to step S200 to determine the probability that thetransaction was a coached transaction based on the behavioral parametersreceived thus far. In such embodiments, if at step S204 it is determinedthat the predetermined threshold hasn't been met, the flow returns tostep S200, to await collection of one or more additional behavioralparameters of the specific set of behavioral data, and thereafter theclassification and evaluation of steps S202 and S204 may be repeated.

In other embodiments, while the behavioral parameters are collected in astaggered manner at step S200, an in some cases also transmitted to thecentral server in a staggered manner, the determination of a probabilityat step S202 and the comparison of the probability to the predefinedthreshold at step S204 occur only once, after all the behavioralparameters have been received. For example, the system may know whatbehavioral parameters are expected, and wait to receive input for allthose parameters. As another example, the system may set a predefinedtime threshold such that if additional data is not received within apredetermined duration, the data set is considered to have beencompleted.

In yet other embodiments, the behavioral parameters collected at stepS200 are collected at once, or are collected in a staggered manner butdelivered to the central server at once, following collection of thewhole data set, including all the behavioral parameters of the entiretransaction. In such embodiments, the determination of probability atstep S202 and the comparison of the probability to the predefinedthreshold at step S204 occur only once.

In cases in which steps S202 and S204 occur only once, if at step S204it is determined that the probability of the online transaction being acoached transaction does not exceed the predefined threshold, the methodmay terminate. Alternatively, the method may include an additional stepS210 in which an indication is provided that the transaction was a safeand valid transaction (i.e. safe from a behavioral perspective andrepresentative of a transaction which lacks coaching by a third partyseeking to illicitly gain funds). The indication may be provided, forexample, to an operator of the central server, for example via an outputinterface thereof, or to the specific user via an output interface of acomputing device used by the user.

In some embodiments, following completion of the safe transaction atstep S210 and/or following denial or nullification of a transaction atstep S206, at step S212, the dataset collected at step S200 is providedto the classification model, for further learning thereof.

Reference is now made to FIG. 1B, which is a flowchart of a method forcreating a multi-dimensional classification model suitable for use inthe method of FIG. 1A according to an embodiment of the disclosedtechnology.

At an initial, preparatory step S250, a plurality of training sets ofbehavioral data relating to the behavior of one or more users during oneor more corresponding online transactions are collected. The trainingsets are typically collected by one or more computing devices on whichthe one or more transactions are carried out, and in some embodimentsmay then be transferred to a single training data origin device. Eachtraining set of behavioral data is associated with a classificationindicating whether or not that training set was generated when the userwas coached during the corresponding online transaction.

At step S252, at least some of the collected training sets of behavioraldata are provided to a central server. In such embodiments, the trainingsets are received from training data origin(s), and are the trainingsets that were collected at step S250. The classifications correspondingto each of the training sets of behavioral data may be received at stepS254.

At step S256, a multi-dimensional classification model is generated, andis trained using the plurality of training sets of behavioral datareceived at step S252, and their corresponding classifications receivedat step S254.

In some embodiments, the plurality of training sets of behavioral datacollected at step S250 relate to the behavior of a single user, duringmultiple online transactions. In some embodiments, the plurality oftraining sets of behavioral data collected at step S250 relate to thebehavior of multiple users, and are collected during one or more onlinetransactions conducted by each of the multiple users.

In some embodiments, at least one of the plurality of training sets ofbehavioral data relates to the specific user operating a specificcomputing device. In some such embodiments, the specific computingdevice may be one of the training data origins, or may be incommunication with one of the training data origins for transmission ofone or more collected training sets of behavioral data and correspondingclassifications thereto. In some such embodiments, the specificcomputing device forms part of a packet switched network, or is incommunication with one of training data origins via another packetswitched network.

Typically, each of the plurality of training sets used to generate theclassification model, collected at step S250 of FIG. 1B and the specificset of behavioral data of the specific transaction collected at stepS200 of FIG. 1A includes at least two behavioral parameters selectedfrom the group consisting of:

-   a total timespan from selecting a text field for input thereinto, to    leaving the text field, for at least one of a text field relating to    a recipient account identifier, a text field relating to a recipient    name, and a text field relating to an amount;-   a number of times during a corresponding online transaction that a    corresponding user stops moving a cursor;-   a number of times during a corresponding online transaction that at    least one of a plurality of cursor criteria is outside of a    corresponding predetermined range;-   a timespan between selecting the text field relating to a recipient    name and beginning to enter input into the text field relating to a    recipient name;-   a total time spent on a monetary transfer page during the    corresponding online transaction;-   a total time during which a cursor was immobile while interacting    with the monetary transfer page during the corresponding online    transaction;-   a timespan between selecting the text field relating to a recipient    account identifier and beginning to enter input into the text field    relating to a recipient account identifier; and-   a number of cursor engagements in the monetary transfer page during    the corresponding online transaction.

In some embodiments, each data set may include at least three, at leastfour, or all of the behavioral parameters listed above, and/or multipleinstances of any one or more of the behavioral parameters listed above.

In some embodiments, the input interface(s) of computing device used forcollection of the datasets include a mouse. In some such embodiments,the cursor engagements comprise mouse clicks. In some such embodiments,the cursor criteria include, for a specific mouse gesture, any one ormore of the following criteria:

-   a ratio between the shortest distance between two endpoints of the    specific mouse gesture and the length of the specific mouse gesture;-   a linearity measure indicating how similar the specific mouse    gesture is to a straight line;-   a ratio between the length of the specific mouse gesture and the    length of a perimeter of a rectangle enclosing the specific mouse    gesture;-   a maximal change in the x-direction during the mouse gesture; and-   a maximal change in the y-direction during the mouse gesture.

In some embodiments, any one or more of the plurality of training setsused to generate the classification model and collected at step S250 ofFIG. 1B and the specific set of behavioral data collected at step S200of FIG. 1A, or each of these sets of behavioral data, may additionallyinclude one or more additional behavioral parameters selected from thegroup consisting of:

-   a timespan between selecting the text field relating to an amount of    the transaction and beginning to enter input into that text field    relating to the amount;-   a sum of all timespans of all cursor movements while on the monetary    transfer page during the corresponding online transaction;-   a measure of the variability of ratios between the length of a    specific cursor gesture or motion and the length of a perimeter of a    rectangle enclosing the specific cursor gesture;-   a count of the total number of cursor gestures;-   a measure of the variability of straightness of cursor gestures or    motions;-   a number of changes of horizontal direction which occur during    cursor motions;-   an average speed of moving the cursor in all recorded cursor    gestures;-   a number of keystrokes in a text field relating to the recipient    account identifier, including typing errors and corrections thereof;-   a total timespan from leaving the text field relating to the amount    of the transaction to a time of selecting a next text field for    input thereinto;-   a number of times the ‘backspace’ or ‘delete’ keys are used while    filling in the text field relating to the recipient account    identifier;-   an average of ratios between the length of a specific cursor gesture    or motion and the length of a perimeter of a rectangle enclosing the    specific cursor gesture, for all cursor gestures;-   an average of the distance traveled with the cursor on the screen    during a single cursor gesture;-   a measure of the variability of ratios between the shortest distance    between two endpoints of the specific cursor gesture and the length    of the specific cursor gesture;-   a measure of the variability of speeds of cursor gestures;-   a length of one or more cursor movements between selecting the text    field relating to a recipient identifier of the transaction and    beginning to enter input into that text field relating to the    recipient identifier;-   a sum of the lengths of all cursor gestures;-   a number of keystrokes in a text field relating to the amount of the    transaction, including typing errors and corrections thereof;-   a number of times the ‘backspace’ or ‘delete’ keys are used while    filling in the monetary transfer page of the corresponding online    transaction;-   an average time duration between two consecutive cursor gestures    while on the monetary transfer page of the corresponding online    transaction;-   an average measure of the straightness of all recorded cursor    gestures;-   a ratio of the sum of time between each pair of consecutive cursor    gestures and the total time spent on the monetary transfer page of    the corresponding online transaction;-   a number of times the text field relating to an amount of the    transaction is selected for insertion of data thereinto during    navigating on the monetary transfer page of the corresponding online    transaction;-   a ratio between the number of times during a corresponding online    transaction that at least one of a plurality of cursor criteria is    outside of a corresponding predetermined range and a theoretical    maximum number of times it is possible for all of the plurality of    cursor criteria to be outside of the corresponding predetermined    range;-   a total timespan from leaving the text field relating to the    recipient identifier to a time of selecting a next text field for    input thereinto;-   a total number of keystrokes during the time spent on the monetary    transfer page of the corresponding online transaction, including    typing errors and corrections thereof;-   an average length of cursor gestures between two timestamps, both of    the two timestamps occurring before selecting the text field    relating to the amount of the transaction and beginning to enter    input into that field;-   a number of changes of vertical direction which occur during cursor    motions;-   a number of times the ‘backspace’ or ‘delete’ keys are used while    filling in the text field relating to the recipient identifier;-   a number of keystrokes during filling in the text field relating to    the recipient identifier, including typing errors and corrections    thereof;-   a number of times the ‘TAB’ key was used while on the monetary    transfer page of the corresponding online transaction;-   a total timespan from leaving the text field relating to the    recipient account identifier to a time of selecting a next text    field for input thereinto;-   an average timespan of a single cursor gesture;-   a length of cursor movements between selecting of the text field    relating to the recipient account identifier and beginning typing in    that text field;-   an average length of cursor gestures between two timestamps, both of    the two timestamps occurring before selecting the text field    relating to the recipient account identifier and beginning to enter    input into that field;-   a number of times the text field relating to the recipient account    identifier is selected for insertion of data thereinto during    navigating on the monetary transfer page of the corresponding online    transaction; and-   a number of times the ‘backspace’ or ‘delete’ keys are used while    filling in the text field relating to the amount of the transaction    identifier.

Reference is now made to FIG. 2A, which is a block diagram of a systemfor denying or nullifying a specific online transaction carried outwhile the user was coached by a fraudster according to embodiments ofthe disclosed technology.

The system 100 includes a device 110, also termed a server 110 herein,for identifying a coached fraudulent transaction. Server 110 isconnected, via one or more packet switched networks 112, to at least onetraining data origin 114, adapted to provide to server 110 a pluralityof sets of behavioral data for a generating and training aclassification model for classifying a probability that an onlinetransaction is a coached fraudulent transaction. Server 110 is furtherconnected, via a packet switched network 116, to at least one useroperated computing device 120, adapted to be used by a specific user tocarry out a specific online transaction.

In some embodiments, packet switched networks 112 and 116 may be asingle packet switched network.

Server 110 typically includes at least one network interface forcommunication to packet switched networks 112 and/or 116, a serverprocessor 132 in communication with the network interface, and a servernon-transitory computer readable storage medium 134 storing instructionsfor execution by server processor 132. For example, storage medium 134may store instructions for carrying out steps S202, S204, S206, S208,and/or S210 of FIG. 1A, and/or instructions for carrying out steps S252,S254, and/or S256 of FIG. 1B.

For example, during carrying out of steps S252 and S254, the pluralityof training sets of behavioral data and the correspondingclassifications may be received from the one or more training dataorigins 114. In some embodiments, the training sets of behavioral datamay be collected on a single computing device forming a training dataorigin, and received by the server 110 as one or more transmissions fromthe single training data origin 114. In other embodiments, the trainingsets of behavioral data may be provided from multiple computing deviceson which these training sets of data were collected, and may be receivedby the server 110 as multiple transmissions from multiple training dataorigins.

In some such embodiments, server 110 may be associated with an outputinterface 126, such as a screen or audio speaker, for providing outputto an operator, or may include a communication interface for contactingenforcement authorities, for example when carrying out steps S206, S208,and/or S210.

FIG. 2B shows a high level block diagram of devices used to carry outembodiments of the disclosed technology. Device 300 comprises aprocessor 350 that controls the overall operation of the computerizeddevice by executing the device's program instructions which define suchoperation. The device's program instructions may be stored in a storagedevice 320 (e.g., magnetic disk, database) and loaded into memory 330when execution of the console's program instructions is desired. Forexample, the storage device 320 may store instructions for collecting aset of behavioral data during an online transaction. Thus, the device'soperation will be defined by the device's program instructions stored inmemory 330 and/or storage 320, and the console will be controlled byprocessor 350 executing the console's program instructions.

A device 300 also includes one or a plurality of input networkinterfaces for communicating with other devices via a network (e.g., theinternet). The device 300 further includes an electrical inputinterface. A device 300 also includes one or more output networkinterfaces 310 for communicating with other devices. For example, theoutput network interfaces 310 may facilitate communication betweendevice 300 and the central server.

Device 300 also includes input/output 340 representing devices whichallow for user interaction with a computer (e.g., display, keyboard,mouse, speakers, buttons, etc.). Such input devices may be used when theuser interacts with the computerized device during the onlinetransaction, such that the data relating thereto can be collected by theprocessor.

One skilled in the art will recognize that an implementation of anactual device will contain other components as well, and that FIG. 3 isa high-level representation of some of the components of such a devicefor illustrative purposes. It should also be understood by one skilledin the art that the method and devices depicted in FIGS. 1A through 2Amay be implemented on a device such as is shown in FIG. 2B.

While the disclosed technology has been taught with specific referenceto the above embodiments, a person having ordinary skill in the art willrecognize that changes can be made in form and detail without departingfrom the spirit and the scope of the disclosed technology. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. All changes that come within the meaning and rangeof equivalency of the claims are to be embraced within their scope.Combinations of any of the methods and apparatuses described hereinaboveare also contemplated and within the scope of the invention.

1. A method for denying or nullifying a specific online transaction,carried out by a specific user using a computing device associated withat least one input interface, while the specific user was coached by afraudster, the method comprising: collecting, from said computingdevice, a specific set of behavioral data relating to the behavior ofthe specific user during a specific online transaction, the specificuser being authorized for carrying out said specific online transaction;using a multi-dimensional classification model, determining aprobability that said specific set of behavioral data was collectedwhile said specific user was coached by a third party; comparing saidprobability to a predefined threshold; and in response to saidprobability being greater than said predefined threshold, indicative ofsaid specific user having been coached during said specific onlinetransaction, denying or nullifying said specific online transaction,wherein said multi-dimensional classification module is trained prior tosaid determining, using a plurality of training sets of behavioral datarelating to the behavior of one or more users during an onlinetransaction, where each specific training set is associated with aclassification indicating whether said specific training set wasgenerated when the corresponding user was coached during thecorresponding online transaction, and wherein each of said plurality oftraining sets and said specific set of behavioral data includes at leasttwo behavioral parameters selected from the group consisting of: a totaltimespan from selecting a text field for input thereinto, to leaving thetext field, for at least one of a text field relating to a recipientaccount identifier, a text field relating to a recipient name, and atext field relating to an amount; a number of times during acorresponding online transaction that a corresponding user stops movinga cursor; a number of times during a corresponding online transactionthat at least one of a plurality of cursor criteria is outside of acorresponding predetermined range; a timespan between selecting saidtext field relating to a recipient name and beginning to enter inputinto said text field relating to a recipient name; a total time spent ona monetary transfer page during said corresponding online transaction; atotal time during which a cursor was immobile while interacting withsaid monetary transfer page during said corresponding onlinetransaction; a timespan between selecting said text field relating to arecipient account identifier and beginning to enter input into said textfield relating to a recipient account identifier; and a number of cursorengagements in said monetary transfer page during said correspondingonline transaction.
 2. The method of claim 1, wherein the at least oneinput interface includes a mouse and wherein: said cursor engagementscomprise mouse clicks; and said cursor criteria include, for a specificmouse gesture, at least one of the following criteria: a ratio betweenthe shortest distance between two endpoints of said specific mousegesture and the length of said specific mouse gesture; a linearitymeasure indicating how similar said specific mouse gesture is to astraight line; a ratio between said length of said specific mousegesture and the length of a perimeter of a rectangle enclosing saidspecific mouse gesture; a maximal change in the x-direction during saidmouse gesture; and a maximal change in the y-direction during said mousegesture.
 3. The method of claim 1, wherein said specific onlinetransaction is a banking transaction.
 4. The method of claim 1, whereinsaid specific set of behavioral data includes data relating to theentirety of said specific online transaction.
 5. The method of claim 1,wherein said collecting and said determining are carried out in realtime, during said specific online transaction, and wherein, in responseto said probability being greater than said predefined threshold, saidspecific online transaction is denied.
 6. The method of claim 1, whereinsaid determining is carried out following completion of said specificonline transaction, and wherein, in response to said probability beinggreater than said predefined threshold, said specific online transactionis nullified.
 7. The method of claim 1, further comprising, followingsaid denying or said nullifying, notifying at least one of said specificuser, a party with whom said specific transaction was carried out, or anenforcement authority, of said denying or said nullifying.
 8. The methodof claim 1, further comprising, following said collecting and prior tosaid determining, transmitting at least part of said specific set ofbehavioral data to a central server, and wherein said determining iscarried out at said central server.
 9. The method of claim 1, wherein:said collecting comprises collecting at least part of said specific dataset; and said collecting, said determining, and said comparing arecarried out iteratively until said probability exceeds said predefinedthreshold or until all data in said data set is collected.
 10. Themethod of claim 1, further comprising, in response to said probabilitybeing lower than or equal to said predefined threshold, indicative ofsaid specific transaction being an authorized transaction, providing asafe transaction notification.
 11. The method of claim 10, wherein saidproviding comprises providing said notification to at least one of saidspecific user, a party with whom said specific transaction was carriedout, or an enforcement authority that said specific transaction was asafe transaction.
 12. The method of claim 1, wherein in said response tosaid probability being greater than said predefined threshold, beforesaid denying or nullifying said specific online transaction, sendingdata via a network to said computing device sufficient to prompt saidspecific user to warn said user of a potential fraudulent or coachedtransaction, or querying said user for whether said user is talking to athird party, and carrying out said denying or nullifying of saidspecific online transaction based on a response or lack of responsewithin a set time period received from said specific user via saidnetwork.
 13. The method of claim 1, wherein in said step of determininga probability that said specific set of behavioral data was collectedwhile said specific user was coached by a third party, said specific setof behavioral data is sufficient to confirm that said specific usercarrying out said specific online transaction is the authorized user.14. A network designed to denying or nullify a specific onlinetransaction initiated or attempted to be initiated by a specific userusing a computing device at a first network node on said network, saidcomputing device associated with at least one input interface, while thespecific user was coached by a fraudster, data sent via said networkincluding, in order: a specific set of behavioral data relating to thebehavior of the specific user during a specific online transaction, thespecific user being authorized for carrying out said specific onlinetransaction; using a multi-dimensional classification model, determininga probability that said specific set of behavioral data was collectedwhile said specific user was coached by a third party; sending datacomparing said probability to a predefined threshold; and in response tosaid probability being greater than said predefined threshold,indicative of said specific user having been coached during saidspecific online transaction, sending data causing a denial ornullification of an attempted transaction sent or to be sent via saidnetwork, wherein said multi-dimensional classification module is trainedprior to said determining, using a plurality of training sets ofbehavioral data relating to the behavior of one or more users during anonline transaction, where each specific training set is associated witha classification indicating whether said specific training set wasgenerated when the corresponding user was coached during thecorresponding online transaction, and wherein each of said plurality oftraining sets and said specific set of behavioral data includes at leasttwo behavioral parameters selected from the group consisting of: a totaltimespan from selecting a text field for input thereinto, to leaving thetext field, for at least one of a text field relating to a recipientaccount identifier, a text field relating to a recipient name, and atext field relating to an amount; a number of times during acorresponding online transaction that a corresponding user stops movinga cursor; a number of times during a corresponding online transactionthat at least one of a plurality of cursor criteria is outside of acorresponding predetermined range; a timespan between selecting saidtext field relating to a recipient name and beginning to enter inputinto said text field relating to a recipient name; a total time spent ona monetary transfer page during said corresponding online transaction; atotal time during which a cursor was immobile while interacting withsaid monetary transfer page during said corresponding onlinetransaction; a timespan between selecting said text field relating to arecipient account identifier and beginning to enter input into said textfield relating to a recipient account identifier; and a number of cursorengagements in said monetary transfer page during said correspondingonline transaction.